Information extraction methods proved to be effective at triple extraction from structured or
unstructured data. The organization of such triples in the form of (head entity, relation, tail …

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Abstract Knowledge graphs are organized as triplets to represent facts from the real world
and play an important role in various intelligent information systems. Because knowledge …

Classifying nodes in knowledge graphs is an important task, eg, for predicting missing types
of entities, predicting which molecules cause cancer, or predicting which drugs are …

Although Transformer has achieved success in language and vision tasks, its capacity for
knowledge graph (KG) embedding has not been fully exploited. Using the self-attention (SA) …

Knowledge graph (KG) embedding seeks to learn vector representations for entities and
relations. Conventional models reason over graph structures, but they suffer from the issues …

Knowledge graphs (KGs) have become a valuable asset for many AI applications. Although
some KGs contain plenty of facts, they are widely acknowledged as incomplete. To address …

Abstract Models computed using deep learning have been effectively applied to tackle
various problems in many disciplines. Yet, the predictions of these models are often at most …

In this paper, we introduce Linked Papers With Code (LPWC), an RDF knowledge graph that
provides comprehensive, current information about almost 400,000 machine learning …

Abstract Concept learning approaches based on refinement operators explore partially
ordered solution spaces to compute concepts, which are used as binary classification …